ForestModelConfig: Object used to get / set parameters and other model...
In stochtree: Stochastic Tree Ensembles (XBART and BART) for Supervised Learning and Causal Inference
ForestModelConfig R Documentation
Object used to get / set parameters and other model configuration options
for a forest model in the "low-level" stochtree interface
Description
The "low-level" stochtree interface enables a high degreee of sampler
customization, in which users employ R wrappers around C++ objects
like ForestDataset, Outcome, CppRng, and ForestModel to run the
Gibbs sampler of a BART model with custom modifications.
ForestModelConfig allows users to specify / query the parameters of a
forest model they wish to run.
Value
Vector of integer-coded feature types (integers where 0 = numeric, 1 = ordered categorical, 2 = unordered categorical)
Vector specifying sampling probability for all p covariates in ForestDataset
Root node split probability in tree prior
Depth prior penalty in tree prior
Minimum number of samples in a tree leaf
Maximum depth of any tree in the ensemble in the model
Scale parameter used in Gaussian leaf models
Shape parameter for IG leaf models
Scale parameter for IG leaf models
Number of unique cutpoints to consider
Public fields
feature_typesVector of integer-coded feature types (integers where 0 = numeric, 1 = ordered categorical, 2 = unordered categorical)
num_treesNumber of trees in the forest being sampled
num_featuresNumber of features in training dataset
num_observationsNumber of observations in training dataset
leaf_dimensionDimension of the leaf model
alphaRoot node split probability in tree prior
betaDepth prior penalty in tree prior
min_samples_leafMinimum number of samples in a tree leaf
max_depthMaximum depth of any tree in the ensemble in the model. Setting to -1 does not enforce any depth limits on trees.
leaf_model_typeInteger specifying the leaf model type (0 = constant leaf, 1 = univariate leaf regression, 2 = multivariate leaf regression)
leaf_model_scaleScale parameter used in Gaussian leaf models
variable_weightsVector specifying sampling probability for all p covariates in ForestDataset
variance_forest_shapeShape parameter for IG leaf models (applicable when leaf_model_type = 3)
variance_forest_scaleScale parameter for IG leaf models (applicable when leaf_model_type = 3)
cutpoint_grid_sizeNumber of unique cutpoints to consider
Create a new ForestModelConfig object.
Methods
Public methods
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Method new()
Usage
ForestModelConfig$new(
feature_types = NULL,
num_trees = NULL,
num_features = NULL,
num_observations = NULL,
variable_weights = NULL,
leaf_dimension = 1,
alpha = 0.95,
beta = 2,
min_samples_leaf = 5,
max_depth = -1,
leaf_model_type = 1,
leaf_model_scale = NULL,
variance_forest_shape = 1,
variance_forest_scale = 1,
cutpoint_grid_size = 100
)
Arguments
feature_typesVector of integer-coded feature types (integers where 0 = numeric, 1 = ordered categorical, 2 = unordered categorical)
num_treesNumber of trees in the forest being sampled
num_featuresNumber of features in training dataset
num_observationsNumber of observations in training dataset
variable_weightsVector specifying sampling probability for all p covariates in ForestDataset
leaf_dimensionDimension of the leaf model (default: 1)
alphaRoot node split probability in tree prior (default: 0.95)
betaDepth prior penalty in tree prior (default: 2.0)
min_samples_leafMinimum number of samples in a tree leaf (default: 5)
max_depthMaximum depth of any tree in the ensemble in the model. Setting to -1 does not enforce any depth limits on trees. Default: -1.
leaf_model_typeInteger specifying the leaf model type (0 = constant leaf, 1 = univariate leaf regression, 2 = multivariate leaf regression). Default: 0.
leaf_model_scaleScale parameter used in Gaussian leaf models (can either be a scalar or a q x q matrix, where q is the dimensionality of the basis and is only >1 when leaf_model_int = 2). Calibrated internally as 1/num_trees, propagated along diagonal if needed for multivariate leaf models.
variance_forest_shapeShape parameter for IG leaf models (applicable when leaf_model_type = 3). Default: 1.
variance_forest_scaleScale parameter for IG leaf models (applicable when leaf_model_type = 3). Default: 1.
cutpoint_grid_sizeNumber of unique cutpoints to consider (default: 100)
Returns
A new ForestModelConfig object.
Method update_feature_types()
Update feature types
Usage
ForestModelConfig$update_feature_types(feature_types)
Arguments
feature_typesVector of integer-coded feature types (integers where 0 = numeric, 1 = ordered categorical, 2 = unordered categorical)
Method update_variable_weights()
Update variable weights
Usage
ForestModelConfig$update_variable_weights(variable_weights)
Arguments
variable_weightsVector specifying sampling probability for all p covariates in ForestDataset
Method update_alpha()
Update root node split probability in tree prior
Usage
ForestModelConfig$update_alpha(alpha)
Arguments
alphaRoot node split probability in tree prior
Method update_beta()
Update depth prior penalty in tree prior
Usage
ForestModelConfig$update_beta(beta)
Arguments
betaDepth prior penalty in tree prior
Method update_min_samples_leaf()
Update root node split probability in tree prior
Usage
ForestModelConfig$update_min_samples_leaf(min_samples_leaf)
Arguments
min_samples_leafMinimum number of samples in a tree leaf
Method update_max_depth()
Update root node split probability in tree prior
Usage
ForestModelConfig$update_max_depth(max_depth)
Arguments
max_depthMaximum depth of any tree in the ensemble in the model
Method update_leaf_model_scale()
Update scale parameter used in Gaussian leaf models
Usage
ForestModelConfig$update_leaf_model_scale(leaf_model_scale)
Arguments
leaf_model_scaleScale parameter used in Gaussian leaf models
Method update_variance_forest_shape()
Update shape parameter for IG leaf models
Usage
ForestModelConfig$update_variance_forest_shape(variance_forest_shape)
Arguments
variance_forest_shapeShape parameter for IG leaf models
Method update_variance_forest_scale()
Update scale parameter for IG leaf models
Usage
ForestModelConfig$update_variance_forest_scale(variance_forest_scale)
Arguments
variance_forest_scaleScale parameter for IG leaf models
Method update_cutpoint_grid_size()
Update number of unique cutpoints to consider
Usage
ForestModelConfig$update_cutpoint_grid_size(cutpoint_grid_size)
Arguments
cutpoint_grid_sizeNumber of unique cutpoints to consider
Method get_feature_types()
Query feature types for this ForestModelConfig object
Usage
ForestModelConfig$get_feature_types()
Method get_variable_weights()
Query variable weights for this ForestModelConfig object
Usage
ForestModelConfig$get_variable_weights()
Method get_alpha()
Query root node split probability in tree prior for this ForestModelConfig object
Usage
ForestModelConfig$get_alpha()
Method get_beta()
Query depth prior penalty in tree prior for this ForestModelConfig object
Usage
ForestModelConfig$get_beta()
Method get_min_samples_leaf()
Query root node split probability in tree prior for this ForestModelConfig object
Usage
ForestModelConfig$get_min_samples_leaf()
Method get_max_depth()
Query root node split probability in tree prior for this ForestModelConfig object
Usage
ForestModelConfig$get_max_depth()
Method get_leaf_model_scale()
Query scale parameter used in Gaussian leaf models for this ForestModelConfig object
Usage
ForestModelConfig$get_leaf_model_scale()
Method get_variance_forest_shape()
Query shape parameter for IG leaf models for this ForestModelConfig object
Usage
ForestModelConfig$get_variance_forest_shape()
Method get_variance_forest_scale()
Query scale parameter for IG leaf models for this ForestModelConfig object
Usage
ForestModelConfig$get_variance_forest_scale()
Method get_cutpoint_grid_size()
Query number of unique cutpoints to consider for this ForestModelConfig object
Usage
ForestModelConfig$get_cutpoint_grid_size()
stochtree documentation built on April 4, 2025, 2:11 a.m.
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| ForestModelConfig | R Documentation |
Object used to get / set parameters and other model configuration options for a forest model in the "low-level" stochtree interface
Description
The "low-level" stochtree interface enables a high degreee of sampler customization, in which users employ R wrappers around C++ objects like ForestDataset, Outcome, CppRng, and ForestModel to run the Gibbs sampler of a BART model with custom modifications. ForestModelConfig allows users to specify / query the parameters of a forest model they wish to run.
Value
Vector of integer-coded feature types (integers where 0 = numeric, 1 = ordered categorical, 2 = unordered categorical)
Vector specifying sampling probability for all p covariates in ForestDataset
Root node split probability in tree prior
Depth prior penalty in tree prior
Minimum number of samples in a tree leaf
Maximum depth of any tree in the ensemble in the model
Scale parameter used in Gaussian leaf models
Shape parameter for IG leaf models
Scale parameter for IG leaf models
Number of unique cutpoints to consider
Public fields
feature_typesVector of integer-coded feature types (integers where 0 = numeric, 1 = ordered categorical, 2 = unordered categorical)
num_treesNumber of trees in the forest being sampled
num_featuresNumber of features in training dataset
num_observationsNumber of observations in training dataset
leaf_dimensionDimension of the leaf model
alphaRoot node split probability in tree prior
betaDepth prior penalty in tree prior
min_samples_leafMinimum number of samples in a tree leaf
max_depthMaximum depth of any tree in the ensemble in the model. Setting to
-1does not enforce any depth limits on trees.leaf_model_typeInteger specifying the leaf model type (0 = constant leaf, 1 = univariate leaf regression, 2 = multivariate leaf regression)
leaf_model_scaleScale parameter used in Gaussian leaf models
variable_weightsVector specifying sampling probability for all p covariates in ForestDataset
variance_forest_shapeShape parameter for IG leaf models (applicable when
leaf_model_type = 3)variance_forest_scaleScale parameter for IG leaf models (applicable when
leaf_model_type = 3)cutpoint_grid_sizeNumber of unique cutpoints to consider Create a new ForestModelConfig object.
Methods
Public methods
Method new()
Usage
ForestModelConfig$new( feature_types = NULL, num_trees = NULL, num_features = NULL, num_observations = NULL, variable_weights = NULL, leaf_dimension = 1, alpha = 0.95, beta = 2, min_samples_leaf = 5, max_depth = -1, leaf_model_type = 1, leaf_model_scale = NULL, variance_forest_shape = 1, variance_forest_scale = 1, cutpoint_grid_size = 100 )
Arguments
feature_typesVector of integer-coded feature types (integers where 0 = numeric, 1 = ordered categorical, 2 = unordered categorical)
num_treesNumber of trees in the forest being sampled
num_featuresNumber of features in training dataset
num_observationsNumber of observations in training dataset
variable_weightsVector specifying sampling probability for all p covariates in ForestDataset
leaf_dimensionDimension of the leaf model (default:
1)alphaRoot node split probability in tree prior (default:
0.95)betaDepth prior penalty in tree prior (default:
2.0)min_samples_leafMinimum number of samples in a tree leaf (default:
5)max_depthMaximum depth of any tree in the ensemble in the model. Setting to
-1does not enforce any depth limits on trees. Default:-1.leaf_model_typeInteger specifying the leaf model type (0 = constant leaf, 1 = univariate leaf regression, 2 = multivariate leaf regression). Default:
0.leaf_model_scaleScale parameter used in Gaussian leaf models (can either be a scalar or a q x q matrix, where q is the dimensionality of the basis and is only >1 when
leaf_model_int = 2). Calibrated internally as1/num_trees, propagated along diagonal if needed for multivariate leaf models.variance_forest_shapeShape parameter for IG leaf models (applicable when
leaf_model_type = 3). Default:1.variance_forest_scaleScale parameter for IG leaf models (applicable when
leaf_model_type = 3). Default:1.cutpoint_grid_sizeNumber of unique cutpoints to consider (default:
100)
Returns
A new ForestModelConfig object.
Method update_feature_types()
Update feature types
Usage
ForestModelConfig$update_feature_types(feature_types)
Arguments
feature_typesVector of integer-coded feature types (integers where 0 = numeric, 1 = ordered categorical, 2 = unordered categorical)
Method update_variable_weights()
Update variable weights
Usage
ForestModelConfig$update_variable_weights(variable_weights)
Arguments
variable_weightsVector specifying sampling probability for all p covariates in ForestDataset
Method update_alpha()
Update root node split probability in tree prior
Usage
ForestModelConfig$update_alpha(alpha)
Arguments
alphaRoot node split probability in tree prior
Method update_beta()
Update depth prior penalty in tree prior
Usage
ForestModelConfig$update_beta(beta)
Arguments
betaDepth prior penalty in tree prior
Method update_min_samples_leaf()
Update root node split probability in tree prior
Usage
ForestModelConfig$update_min_samples_leaf(min_samples_leaf)
Arguments
min_samples_leafMinimum number of samples in a tree leaf
Method update_max_depth()
Update root node split probability in tree prior
Usage
ForestModelConfig$update_max_depth(max_depth)
Arguments
max_depthMaximum depth of any tree in the ensemble in the model
Method update_leaf_model_scale()
Update scale parameter used in Gaussian leaf models
Usage
ForestModelConfig$update_leaf_model_scale(leaf_model_scale)
Arguments
leaf_model_scaleScale parameter used in Gaussian leaf models
Method update_variance_forest_shape()
Update shape parameter for IG leaf models
Usage
ForestModelConfig$update_variance_forest_shape(variance_forest_shape)
Arguments
variance_forest_shapeShape parameter for IG leaf models
Method update_variance_forest_scale()
Update scale parameter for IG leaf models
Usage
ForestModelConfig$update_variance_forest_scale(variance_forest_scale)
Arguments
variance_forest_scaleScale parameter for IG leaf models
Method update_cutpoint_grid_size()
Update number of unique cutpoints to consider
Usage
ForestModelConfig$update_cutpoint_grid_size(cutpoint_grid_size)
Arguments
cutpoint_grid_sizeNumber of unique cutpoints to consider
Method get_feature_types()
Query feature types for this ForestModelConfig object
Usage
ForestModelConfig$get_feature_types()
Method get_variable_weights()
Query variable weights for this ForestModelConfig object
Usage
ForestModelConfig$get_variable_weights()
Method get_alpha()
Query root node split probability in tree prior for this ForestModelConfig object
Usage
ForestModelConfig$get_alpha()
Method get_beta()
Query depth prior penalty in tree prior for this ForestModelConfig object
Usage
ForestModelConfig$get_beta()
Method get_min_samples_leaf()
Query root node split probability in tree prior for this ForestModelConfig object
Usage
ForestModelConfig$get_min_samples_leaf()
Method get_max_depth()
Query root node split probability in tree prior for this ForestModelConfig object
Usage
ForestModelConfig$get_max_depth()
Method get_leaf_model_scale()
Query scale parameter used in Gaussian leaf models for this ForestModelConfig object
Usage
ForestModelConfig$get_leaf_model_scale()
Method get_variance_forest_shape()
Query shape parameter for IG leaf models for this ForestModelConfig object
Usage
ForestModelConfig$get_variance_forest_shape()
Method get_variance_forest_scale()
Query scale parameter for IG leaf models for this ForestModelConfig object
Usage
ForestModelConfig$get_variance_forest_scale()
Method get_cutpoint_grid_size()
Query number of unique cutpoints to consider for this ForestModelConfig object
Usage
ForestModelConfig$get_cutpoint_grid_size()
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